Dear Editor, This letter proposes a contrastive consensus graph learning model for multi-view clustering. Graphs are usually built to outline the correlation between multi-model objects in clustering task, and multiview aims learn that integrates spatial property of each view. Nevertheless, most graph-based models merely consider overall structure from all views but neglect local consistency di...

Background: Researchers need visualization methods (using statistical and interactive techniques) to efficiently perform quality assessments glean insights from their data. Data on networks can particularly benefit more advanced techniques since typical methods, such as node-link diagrams, be difficult interpret. We use heatmaps consensus clustering network data show...

One of the most used techniques among many others in the data mining field is the clustering. The aim of this technique is to synthetize and summarize huge amounts of data by splitting it into small and homogenous clusters such that the data (observations) inside the same cluster are more similar to each other than to the observations inside the other clusters. This definition assumes that ther...

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate sta...

Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of robust and universally applicable cluster validity scores often makes the algorithm selection and hyperparameter evaluation a tough guess. In this paper, we show that cluster ensemble aggregation techniques such as conse...

Cluster Analysis is a field of Data Mining used to extract underlying patterns in unclassified data. Many existing clustering algorithms are inadequate in that they require knowledge of how many clusters exist in the data, otherwise known as k, and that their underlying assumptions make them ineffective in certain situations. The method of Consensus Clustering seeks to rectify the latter proble...

Consensus patterns, like motifs and tandem repeats, are highly conserved patterns with very few substitutions where no gaps are allowed. In this paper, we present a progressive hierarchical clustering technique for discovering consensus patterns in biological databases over a certain length range. This technique can discover consensus patterns with various requirements by applying a post-proces...